Depression Detection on Social Media with Large Language Models: A Novel Approach Combining Medical Knowledge and NLP Techniques

Core Concepts

Combining medical knowledge and advanced NLP techniques, the DORIS system improves depression detection accuracy and interpretability.

Abstract

The article discusses the challenges of detecting depression on social media due to stigma and lack of awareness. It introduces the DORIS system, which combines medical knowledge with large language models for accurate detection. The system addresses the need for both accuracy and explainability in depression detection by annotating high-risk texts and summarizing mood courses. Experimental results show a significant improvement in AUPRC compared to baselines, validating the effectiveness of the approach.

Stats

Extensive experimental results show an improvement of 0.036 in AUPRC.
The DORIS system integrates DSM-5 diagnostic criteria for depression.
The method combines features from different spaces using a GBT classifier for high accuracy.

Quotes

"Our contribution can be summarized as follows: We are among the first to combine professional medical knowledge and advanced large language models in depression detection."
"We propose the depression detection system DORIS, aiming to integrate professional medical knowledge with advanced NLP techniques to generate accurate and interpretable judgments."

Key Insights Distilled From

Depression Detection on Social Media with Large Language Models

by Xiaochong La... at arxiv.org 03-19-2024

https://arxiv.org/pdf/2403.10750.pdf

Deeper Inquiries

How can the DORIS system be adapted for detecting other mental health conditions?

The DORIS system can be adapted for detecting other mental health conditions by incorporating diagnostic criteria specific to those conditions. Just as it utilizes the DSM-5 criteria for depression detection, similar diagnostic frameworks exist for various mental health disorders like anxiety disorders, bipolar disorder, PTSD, and more. By integrating these diagnostic standards into the system and adjusting the annotation prompts and feature extraction processes accordingly, DORIS can effectively identify symptoms indicative of different mental health conditions. Additionally, training the model on datasets that include a diverse range of mental health conditions would enable it to learn patterns associated with each condition.

What ethical considerations should be taken into account when deploying AI systems for mental health analysis?

When deploying AI systems for mental health analysis, several ethical considerations must be taken into account:

Privacy: Ensuring user data privacy is crucial in handling sensitive information related to mental health.
Informed Consent: Users should provide informed consent before their data is used for analysis.
Transparency: The AI system's decision-making process should be transparent and explainable to build trust with users.
Bias Mitigation: Efforts should be made to mitigate biases in the data or algorithms that could lead to unfair outcomes or perpetuate existing disparities.
Data Security: Implementing robust security measures to protect user data from breaches or unauthorized access.
Human Oversight: Having human experts involved in interpreting results and making final decisions based on AI recommendations.

How can the DORIS system be improved further to enhance its real-world applicability?

To enhance the real-world applicability of the DORIS system, several improvements can be considered:

Multi-Class Detection: Expand beyond depression detection to include a broader range of mental health conditions.
Continuous Learning: Implement mechanisms for continuous learning from new data sources to adapt and improve over time.
User Interaction Features: Incorporate features that allow users to interact with the system directly through chatbots or interfaces tailored for mental health support.
Integration with Healthcare Systems: Integrate seamlessly with healthcare providers' systems for streamlined diagnosis and treatment planning.
5Cross-Domain Analysis: Extend analysis beyond social media posts by incorporating additional sources such as electronic medical records or wearable device data.

These enhancements would make DORIS more versatile and effective in supporting comprehensive mental health analysis in diverse settings."